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SHARIF UNIVERSITY OF TECHNOLOGY, INTERNATIONAL CAMPUS RESEARCH BULLETIN

A Quarterly Publication of School of Science and Engineering

EDITOR IN CHIEF

Alireza Ghorshi

EDITORIAL BOARD

Masoud Askari, Sharif University of Technology, International Campus Habib Bagheri, Sharif University of Technology Morteza Eskandari, Sharif University of Technology, International Campus Kambiz Ghaemi Osgouie, Sharif University of Technology, International Campus Alireza Ghorshi, Sharif University of Technology, International Campus Saeed Hashemi, Sharif University of Technology, International Campus Mohsen Jahanshahi, Sharif University of Technology, International Campus Mehran Jahed, Sharif University of Technology Siamak Kazemzadeh Hannani, Sharif University of Technology Mohammad Khansari, Sharif University of Technology, International Campus Amir Ali Khayyat, Sharif University of Technology, International Campus Mohammad Taghi Manzuri, Sharif University of Technology Seyed Abolghasem Miremadi, Sharif University of Technology

Seyed Mohammad Mortazavi, Sharif University of Technology, International Campus Seyed Reza Mousavi, Sharif University of Technology, International Campus Abolghasem Najafi, Sharif University of Technology, International Campus Alireza Nemaney Pour, Sharif University of Technology, International Campus Saeed Orangi, Sharif University of Technology, International Campus Mohsen Sadighi, Sharif University of Technology, International Campus Mahdi Sani, Sharif University of Technology, International Campus Ali Selk Ghaffari, Sharif University of Technology, International Campus Mohammad Reza Shams, Sharif University of Technology, International Campus Hossein Shodja, Sharif University of Technology Mohammad Ali Vesaghi, Sharif University of Technology Abolghasem Zabihollah, Sharif University of Technology, International Campus Hossein Zaman, Sharif University of Technology, International Campus

Aims and Scope

The major objective of the bulletin of research is to archive the current and most recent research activities of faculty members and post-graduate students of SUT-International Campus and to distribute them in order to facilitate the communication of research outcomes between scholar public. Basically, the papers that have already been published or accepted for publication in journals and conferences are reviewed by editorial board and presented in the bulletin; however research high quality original/unpublished papers are also welcomed. The scope of the bulletin congruent with the graduate programs of the school is multi-disciplinary. It covers analytical, numerical and experimental papers in the fields of Engineering and Science from both industrial oriented aspects and leading edge research endeavours.

Disclaimer

Publication of papers in this particular research bulletin is for internal use only. It does not imply that the editorial board and reviewers endorse the data and conclusions of authors.

Research Bulletin Vol. 1, No. 1, December 2009

CONTENTS

Civil Engineering M.R. Nemati M.H. Sadighiani

Interaction between Tunnel and Adjacent Structures Using a Two-Dimensional Finite Element Analysis

1-5

Electrical Engineering S. Ziabakhsh H. Alavi-Rad M. Alavi-Rad M. Mortazavi

The Design of a Low-Power High-Speed Current Comparator in 0.35-μm CMOS Technology

6-11

Information Technology M. Khansari H. R. Rabiee M. H. Rohban M. Ghanbari

On the Search Window Updating for Occlusion Handling in Object Tracking Application

12-16

Materials Engineering M. R. Shams H. Pezeshki Modarres

Composite Particleboard Panels Made from Stone powder-Rice straw Properties are Experimented as a New Construction Materials

17-20

Mechatronics Engineering R. Hortamani A. Zabihollah

Modeling and Simulation of Graspers Force in Minimally Invasive Surgery

21-25

F. Mohammadi I. Hemmatian K. Ghaemi Osgouie

Manipulability Analysis for Gimbal Driven Robotic Arms 26-31

Research Bulletin, Sharif University of Technology, International Campus, Kish Island, Vol. 1, No. 1, December 2009

1

Interaction between Tunnel and Adjacent Structures Using a Two-Dimensional Finite Element Analysis

Mohammad R. Nemati, Mohammad H. Sadighiani

Abstract: This paper concerns a study of the interaction between tunneling and adjacent structures. Analysis is performed using a full two-dimensional finite element model, which takes into considera-tion the presence of adjacent structure with regard to distance to tunnel and diameter of tunnel during construction of the tunnel. The soil behavior is assumed to be governed by an elastic perfectly-plastic constitutive relation based on Mohr-Coulomb criterion with a non-associated flow rule. The paper is composed of two parts. However, compatibility of each method with beam and solid continuum ele-ment models in two dimensional finite element (FE) analyses was investigated. The first part describes the numerical model used in this study. The two-dimensional analysis of the construction of a shallow tunnel close to a multi-level building was conducted using the finite element program, ABAQUS. The analyses include influence of effective parameters such as tunnel diameter, width of soil between tun-nel crown and building foundation. The effect of construction stages of structures on the tunnel stabili-ty was also studied. Next, the results of analyses were compared quantitatively. Keywords: Interaction; Plasticity; Structure; Two-dimensional; Tunnelling.

1. INTRODUCTION This paper presents a thorough 2D analysis related to

the interaction between tunnelling and adjacent structure. This problem was previously analyzed using a combination of in situ observations and numerical modelling. Analysis of previous case histories paved the way for the establishment of various empirical relationships between tunnelling induced ground movement and associated structure damage [1–3]. In this paper, a study of the interaction between the construction of a lined tunnel and adjacent structures is described. This study is divided in to two parts: Structure construction before tunnelling and Tunnelling before structure construction. The first part addresses the determination of tunnelling-induced ground movement using empirical, numerical methods such as those proposed by Peck [9], O’Reilly and New [8], Mroueh and Shahrour [7]. The building response to tunnelling is then determined in the second step by performing a complete structural analysis of the building. It should be mentioned Potts and Addenbrooke [10] used a coupled 2D finite element model to study the influence of a surface structure on the ground movement caused by tunnelling. Moreover, Franzius [4] verified their study

and investigated influences of building weight and stiffness over tunnel deformation and the stress regime. It would be more satisfactory to predict the measure which minimizes the impact of the tunnelling works, before planning a structure. However, this optimal situation is hardly feasible both technically and economically due to the uncertainties that remain at the design stage on the ground response to tunnelling and the actual condition of the buildings [6].

Current study is performed using a 2D finite element analysis, which takes into consideration the elastoplastic behaviour of the soil, the tunnelling procedure and the presence of the structure. After a brief review of numerical method, the first part addresses the 2D analysis employed in the construction of a shallow tunnel in the proximity of a 4-story building and compares the influences of tunnel effective parameters. The last part includes the investigation of the structure stages over impressible parameter of tunnel. According to the results of FE analyses, the best pattern among all patterns was achieved with convergence-confinement method using beam elements as shotcrete. Besides, its model predicted the max settlement larger than the other methods [5].

2. NUMERICAL MODELLING

Figure 1 indicates the problem under consideration which is used to quantify the interaction between tunnelling proximity structure. The tunnel is characterized by its depth Hb, diameter D, lining thickness t, while the building is modelled by a spatial reinforced concrete framed structure characterized by the level height H and column’s spacing as S. The behaviour of the structure is assumed to be linear-elastic. The soil behaviour is assumed to be governed by an elastic perfectly-plastic constitutive relation based on the Mohr–Coulomb criterion with a non-associative flow rule.

__________

Manuscript has been presented at 2nd International Conference on Computational Methods in Tunneling in 2009.

Mohammad Reza Nemati, M.Sc student, School of Science and Engineering, Sharif university of Technology, International Campus, Kish Island.

Mohammad H. Sadaghiani, Ph.D, Department of Civil Engineering, Sharif University of Technology, Iran. (Corresponding author to provide phone: +98-21- 66022727 Ext: 4228; fax: +98 -21- 66014828; e-mail: [email protected]).

Research Bulletin, Vol. 1, No. 1, December 2009

2

It is worth noting that such analysis can be improved by employing a more realistic soil material constitutive relation, which takes into account soil hardening and stress-dependant elastic properties. Analysis of the tunnelling–structure interaction problem is performed in two parts. The first part is concerned with the determination of initial stresses in the soil mass prior to the tunnel construction. The second part deals with the numerical analysis for the construction of the tunnel prior to the structure construction. In this paper, numerical simulations were performed by means of the finite element program ABAQUS which provides flexible features for the analysis of 2D/3D and non-linear soil–structure interaction problems.

3. 2D FINITE ELEMENT ANALYSIS

3.1. Presentation of the example-first part Table 1 depicts all of specification of soil, structure

and tunnel especially the variable effective parameters (diameter and depth) are wholly defined. Besides, the thickness, elasticity modulus and Poisson’s ratio of concrete lining are 50cm, 35000MPa and 0.2 respectively and also constant for both parts of this study. Finite element analysis is carried out using the mesh approximately between 16000 to 25000 elements, for different cases. In order to simulate such a relative movement interface elements are introduced in this study. The concrete lining of tunnel is modelled as Timoshenko beams with zero thickness.

For the first part of this paper the structure is a 4-story building and the profiles of HE300 B and IPE240 are selected for the columns and beams sections successively.

Tunnel excavation steps are modelled in two steps for whole cases. Primary, the first effective parameters (tunnel diameter) increases from 6m to 9m with constant tunnel depth.

Lastly, the width of soil between tunnel crown and

ground surface (tunnel depth) grows from 9m to 24m, but tunnel diameter is 9m for all of them. Of course, the specifications and dimensions of the whole models are changeless. Meantime, the structure construction is prior to tunnel construction. Whereas, variation of mentioned parameters effects over impressible parameters like as surface settlements, tunnel deformations, plastic zones, axial force of structure’s columns, structure’s beams deformations and columns’ bending moments; therefore, each impressible parameters are investigated separately in the following section. 3.2. Presentation of the example-second part

Here the stages of structure construction (the numbers of building’s floor vary from 1 to 5) are developed during of this study. On the other hand, the tunnelling is prior to structure construction. However, the rest specification of soil, structure and tunnel are remained constant for all cases. As a matter of fact, the growth of floors influences differently over the impressible parameters as surface settlements, tunnel principal stresses, tunnel deformations and plastic zones which are concerned in the following sections. In addition, the entire analysis is performed in drained condition. Computation is carried out in ten successive steps for the structure construction and excavation modelling.

4. TUNNELLING-STRUCTURE INTERACTION-

PART ONE

4.1. Surface settlement, tunnel deformation and plastic zones

Figure 2 presents surface settlements of tunnel for both effective parameters. It should be noted when tunnel depth of Hb=12m is constant and tunnel diameter in-creases from 6m to 9m, tunnel diameter of D=9m has maximum settlement and is about 19mm. On the other

Figure 1 Tunneling–structure interaction: problem under consideration


3

hand, while tunnel diameter of D=9m is constant and tunnel depth increases from 9m to 24m, surface settle-ment decreases from 32mm to 12mm.

Table 2 shows the growth of tunnel diameter in-creases tunnel’s crown deformation from 6mm to 23mm, vice versa the growth of tunnel depth reduces it from 35mm to 20mm. However, the growth of tunnel diameter and depth cause an increase on the plastic zones circum-ference of tunnel and even beneath structure foundation. 4.2. Structure elements Table 3 indicates the increment percentages of axial force and bending moments for external and internal column, separately. Whereas, these models have sym-metric geometry, therefore centreline column changes indiscernible. However, the growth of tunnel diameter and the diminution of tunnel depth increase axial force and bending moment in the external columns (C1/C5). Meantime, increment magnitudes in the lower floors are greater than the upper floors in these columns. In con-trast, the growth of tunnel diameter and the diminution of tunnel depth decrease noticeably axial force, but increase bending moment in the internal columns (C2/C4) which have upwards trend toward the upper floors unlike the external columns. In addition, the growth of tunnel di-ameter and the diminution of tunnel depth increase beam deformation especially in the beams which are located between middle columns.

5. TUNNELING-STRUCTURE INTERACTION PART-TWO

The purpose of this section is to investigate about

impressible tunnel parameters, while tunnelling is prior to the structure construction. Whereas, tunnel construc-tion influence ground surface, therefore it is subsided according Smax (Peck, 1969) [9], but it continues until stages of structure are completed. However, tunnel diame-ter of D=9m and tunnel depth of Hb=9m are constant in the whole steps. It should be noted that the growth of numbers of building’s floors from 1-story to 5-story and subsequence loading increases settlement from 3.5mm t0 17.4mm in Figure 3. Furthermore, the principal tunnel stresses in-creases especially in the tunnels crown. On the other hand, the growth of numbers of building’s floors from 1-story to 5-story increase tunnel deformation from 4.9mm to 16.3mm in the tunnel crown and decrease from 5.3mm to 4.6mm in the bottom of tunnel. Lastly, the growth of numbers of building’s floors from 1-story to 5-story increase plastic zones in the tunnel cir-cumference in Figure 4 (a) to 4 (e).

a) 1-story b) 2-story c) 3-story

d) 4-story e) 5-story

Figure 4 Plastic zones of tunnel circumference for 1- to 5-storey building

‐35

‐30

‐25

‐20

‐15

‐10

‐5

0

‐40 ‐20 0 20 40

Sett

lem

ent (

mm

)

Displacement of Centerline of Tunnel (m)

D=6m, Hb=12mD7.5m, Hb=12mD=9m, Hb=9mD=9m, Hb=12mD=9m, Hb=24m

Figure 2 Comparison between settlements of differenttunnel diameter and depth

-18.0

-16.0

-14.0

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

-40 -30 -20 -10 0 10 20 30 40

grou

nd S

ettle

men

t (m

m)

Displacement of Centerline of Tunnel (m)

First step of excavationSecond step of excavationExcution of Final supportCutting undergroundAfter construction of first floorAfter construction of second floorAfter construction of third floorAfter construction of fourth floorAfter construction of fifth floor

Figure 3 Comparison of surface settlement between 1- to 5-storey buildings and the whole tunnelling steps.


4

Table 1 Specification and values of parameters of tunnel, soil and structure for the first part

Parts of Model Parameters and Values

Soil Mechanic

Parameter γ(N⁄m3) E(MPa) c(kPa) ν φ(deg) ψ(deg)

Value 20000 500 50 0.35 30 5

Structure Position Value of

loading Number of

underground storey

Total numbers of

stories

Number of columns

Distance of columns(m)

height of stories (m)

Constructed 700 kg/m2 1 4 5 4 3

Tunnel (circular)

Variable Parameters

First effective

Parameter D(m)

First Case Second Case Third Case

6 7.5 9

Second effective

Parameter Hb(m)

First Case Second Case Third Case

9 12 24

Constant Parameters

Parameter Horizontal distance between centreline of tunnel and structure/e(m)

Distance of centre of tunnel and boundary condition of soil (m)

Number of Excavation's step

Value 0 3.5D 2

6. CONCLUSION A numerical study of the interaction between

tunnelling and adjacent structure is introduced in this paper. Numerical simulations were conducted using 2D calculation which takes into account the presence of the structure in two different conditions. In the first case, the structure is prior to tunnelling and in the next case, tunnelling is prior to the structure. Present analysis indicates that tunnelling-induced forces drastically depend on the presence of adjacent structures. However, the presence of structure prior to tunnelling influence on the settlement and plastic zone beneath structure. Furthermore, present analysis show that it is far too interesting to consider substantially the growth tunnel diameter and depth in the trend determination of surface settlement, deformation and principal stresses of tunnel and also in the structure elements. Finally, neglect of the structure may generally lead to notable underestimation of the tunneling-induced forces and incorrect computation of settlement and subsequent unreal internal forces in the structure.

REFERENCES

[1] Boscardin MD, Cording EG. Building response to excavation induced settlement. ASCE Journal of Geotechnical Engineering 1989; 115(1):1–21.

[2] Burland JB. Assessment of risk damage to buildings due to tunnelling and excavation. In: Proceedings of 1st International Conference on Earthquake and Geotechnical Engineering, IS-Tokyo; 1995.

[3] Burland JB, Wroth CP. Settlements on buildings and associated damage. In: Proceedings of Confer-ence on Settlement of structures. Cambridge: BTS; 1974. p. 611–54.

[4] Franzius J.N., Behaviour of buildings due to tunnel induced subsidence, Department of Civil and Envi-

ronmental Engineering, Imperial College of Sci-ence, Technology and Medicine, London, 2003.

[5] Karakus M, Appraising the methods accounting for 3D tunnelling effects in 2D plane strain FE analysis, Tunnelling and Underground Space Technology, 22, [2007], 47–56.

[6] Leca E, New B, ITA WG Research, Settlements induced by tunnelling in Soft Ground, Tunnelling and Underground Space Technology, 22, [2007], 119–149.

[7] Mroueh H., Shahrour I., A full 3D finite element analysis of tunnelling–adjacent structures interac-tion, Journal of Computers and Geotechnics, 30, [2003], 245-253.

[8] O’Reilly MP, New BM. Settlements above tunnels in United Kingdom—their magnitude and predic-tion. In: Proceedings of Tunnelling’82, London; IMM; 1982. p. 173–81.

[9] Peck RB. Deep excavation and tunnelling in soft ground. In: 7th International Conference on Soil Mechanics and Foundations Engineering, Mexico City, State-of-Art, 1969. p. 225–290.

[10] Potts DM, Addenbrooke TI. A structure’s influence on tunnelling-induced ground movements. ICE Journal of Geotechnical Engineering 1997; 125(Issue 02):109–25.

[11] Sagaseta C. Evaluation of surface movements above tunnels: a new approach. Colloque Interna-tional ENPC Interactions Soil-Structures, Paris 1987; 1987:445–52.

[12] Zienkiewicz OC, Chan AHC, Pastor M, Schrefler BA, Shiomi T, Computational geomechanics with special reference to earthquake engineering, John Wiley & Sons Ltd, 1999; England.


5

Table 2 Deformation magnitude of tunnel’s different specification

Different tunnel D=6m, Hb=12m

D=7.5m, Hb=12m

D=9m, Hb=9m

D=9m, Hb=12m

D=9m, Hb=24m

Tunnel’s crown deformation(mm) 6 11 35 23 20

Table 3 Increment and reduction percentages of columns axial force and bending moments

Different tunnel D=6m Hb=12m

D=7.5m Hb=12m

D=9m Hb=9m

D=9m Hb=12m

D=9m Hb=24m

Axial force of (C1/C5) for underground 3 9 46 28 4

Axial force of (C1/C5) for the last floor 2 7 40 25 3

Axial force of (C2/C4) for underground -1 -4 -20 -14 -1

Axial force of (C2/C4) for the last floor -1 -3 -17 -13 -1

Bending Moment of (C1/C5) for underground 32 61 176 130 70

Bending Moment of (C1/C5) for the last floor 8 24 126 80 10

Bending Moment of (C2/C4) for underground 140 280 1100 570 270

Bending Moment of (C2/C4) for the last floor 110 286 1600 840 130


6

The Design of a Low-Power High-Speed Current Comparator in 0.35-μm CMOS Technology

Soheil Ziabakhsh, Hosein Alavi-Rad, Mohammad Alavi-Rad, Mohammad Mortazavi

Abstract: A novel low power with high performance low current comparator is proposed in this paper which comprises of low input impedance using a simple biasing method. It aimed for low power consumption and high speed designs compared with other high speed designs. The simulation results from HSPICE demonstrate the propagation delay is about 0.7 ns and the average power consumption is 130 μW for 100 nA input current at supply voltage of 1.8 V using 0.35 micron CMOS technology. Keywords: Current Comparator, Propagation Delay, Instantaneous Power, Positive Feedback, Signal Processing .

1. INTRODUCTION Current comparators are important building blocks

within many analogue circuit designs. In particular, they are used for front-end signal processing applications and increasingly within neuromorphic electronic systems [1,2]. Low voltage and low power application demands confront voltage mode IC designs, for there is less dynamic available under low power supply condition. While the circuit implemented in current mode technique occupies small area, consumes less power dissipation and achieves more dynamic range and high operation speed. Thus the current mode circuit design methodology receives increasing wide attention in the recent years [3, 4].

Moreover, many sensors in SoC such as temperature sensors, photo sensors provide current signal. In these applications and high speed data converters, where the function of comparison is a limiting component for accuracy, noise and power consumption reasons, the introduction of current mode solutions is highly desirable. The current comparison process is injecting one or two current flowing into the comparator and distinguishing the current (or the difference of two currents) is positive or negative. The output voltage generated by the output current is used conveniently to indicate the result of the comparison.

The comparison process is relatively simple, but the implementation of the current comparator is becoming more complex. Low input impedance, which is required

by current mode circuits, should be considered first. Secondly, a quick time response is demanded by the current comparator. The main limitation to the time response usually comes from the initial balance of the output branches that often leads to the triode region some output transistors. Finally, the precision of comparator designs are playing an important role in the design requirements, and it depends on the offset caused by the mismatch of transistors. In the recent years, there have been many good implementations reported [5, 6]. However, many of the proposed implementations had only emphasized on one or several aspects at the cost of deterioration in other characteristics. Obviously there is a requirement to transform the input current to a large voltage signal. Thus to design a high speed current comparator, one has to consider the voltage swing carefully since it directly determines the propagation delay.

Conventionally, most reported current comparators [7, 8] are based on the concept shown as a block diagram in Figure 1, where the input current signal is converted to the voltage Vin and V1 by the transimpedance stage comprising inverter amplifier A1 and voltage buffer A2. The resulting voltage V1 is then amplified by the latter high gain inverter amplifier A3 to produce output logic voltage. There exist parasitic capacitors at all nodes.

Ideally for high speed comparators, the signal swing at V1 should be maintained as small as possible and situated exactly around the inverter threshold voltage of the inverter A3. However, the reported works were relating to improve the lowest input current acquiring ability by arranging a proper biasing to turn on the MOSFETs of the buffer A2 all the time.

Most of them utilized diode connected MOSFETs as a level shifter to create VGS of the buffer MOSFETs. It is seen that although the transimpedance stage is formed in a negative feedback loop, a much larger loop gain has not been exploited to keep the signal Vin and V1 as low as possible. Moreover with a larger loop gain, the input impedance at node Vin could be much lower and receive a much smaller input current in the range of Pico-Amps.

__________ Manuscript has been presented at IEEE 10th Int’l Symposium

on Quality Electronic Design. Soheil Ziabakhsh and Hosein Alavi-Rad, M.Sc students,

Faculty of Engineering, University of Guilan, Rasht, Iran. Mohammad Alavi-Rad, M.Sc student, Electrical Engineering

Department, Sharif University of Technology, Tehran, Iran. Mohammad Mortazavi, Ph.D, Department of Electrical,

Sharif University of Technology, International Campus Kish Island, Iran. (Corresponding author to provide phone: +98-764-4422299 Ext: 347; fax: +98-764-4422828; e-mail: [email protected]).


7

The dead zone term which is the smallest input current range to which comparators are insensitive is then minimized. However, a drawback of having the small voltage swing at V1 is that the gain of the latter inverter amplifier must be necessarily high. Hence it yields to higher power consumption. Obviously, there is a conflict that if the speed as a result of a small voltage swing of the tranresistance stage is desired, a very high gain of the latter inverter amplifier will be necessary to provide the rail to rail output swing.

Figure 1 Current Comparator Concept.

2. PREVIEW LOW-POWER HIGH-SPEED CURRENT COMPARATORS

Recently, a number of current comparator circuits

have been reported [9, 10]. The current comparator reported in [9] is perhaps the first current comparator which possesses lower input impedance than previous circuits. In the circuit shown in Figure 2, M1 and M2 form a class B voltage buffer; and M3- M6 form two inverting amplifiers. Iin is the input current, which is the difference between the signal and the reference currents. The circuit has three modes of operation. When Iin is positive, V1 is pulled high.

This is amplified by M3 and M4, causing V2 to go low. VGS1 and VGS2 are negative, turning M1 off and M2 on. In this state, V1 is a low impedance node, because Iin is supplied by M2. When Iin changes its sign, there is insufficient gate drive for the buffer to supply Iin, thus V1 is temporarily a high impedance node. When Iin is negative, V1 is pulled low and V2 is pulled high, turning M1 off and M2 on; again V1 is a low impedance node. The width of this dead band region in the transfer characteristic of the buffer is determined by the threshold voltage of M1 and M2, and the response time of the comparator significantly increases as the input current decreases. The current comparator reported in [10] reduced this dead band by changing the biasing scheme of M1 and M2 from class B to class AB operation. This results in smaller voltage swings at V1 and V2 and hence faster response. However, this comparator requires a complex biasing circuit in order to reduce the dead band, and increase the power consumption.

Therefore, the comparators proposed in [9,10] use

nonlinear positive feedback to enhance the response time, and it can be said that improvement is achieved at the expense of sensitivity and power consumption. The feedback operation of these circuits does not allow the input node to slew from rail to rail. Instead it maintains the operating voltage of the comparator node midway between the threshold voltages of the PMOS and NMOS transistors M1 and M2. This allows high speed operation but consumes high current through transistors M1-M4 as quiescent non-zero DC power consumption.

Figure 2 The Original Current Comparator [9].

One of most significant challenges is to minimize the

dead zone. Figure 3 shows the new circuit which employs one diode-connected NMOS transistor instead of two to provide the voltage drop between the gates of M1 and M2 [11]. The advantages are not only saving one transistor but also reducing the channel width. Here, the (VGS - VTH) value of M4 is proportional to the square root of current through M3, M4 and M5.

Since the input current (Iin) is very small, the variations of current and (VGS - VTH) of M4 are also very small. The input impedance is about 1/ (gm1 + gm2), which is much smaller than that of Figure 1 due to higher VGS . The drain and source of M4 are connected to the gates of M6 and M7, respectively. The purpose is to provide higher current for charging and discharging the gates of M8 and M9, and thus enhance the speed. In order to reduce the current through M6 and M7, the channel lengths were increased to save the power consumption. M8-M11 are a pair of inverters to amplify the output signal.

Figure 3 The Current Comparator Proposed in [11].


8

The problem of using inverters as amplifiers is sensitivity of processes. Since the process may go to the SS, FS, SF and FF comers, the output of M6 and M7 may not be at the right threshold voltage of the inverter M8/M9. Here, S and F stand for slow and fast, respectively. The first character is for NMOS transistors, while the second one is for PMOS. The two transistors Mn and Mp are used to adjust the inverter threshold voltage using different voltage values of Vn and Vp. For the typical case (TT), Vn is equal to VDD and Vp is grounded. A schematic of the positive feedback system proposed in [12] is shown in Figure 4.

Positive feedback operates at the output nodes of the inverters M5/M6 and M7/M8, respectively. In the pre-decision state transistors M2 and M3 are closed and transistors M1 and M4 are open. As the voltage on the comparator node is affected by input current, so the inverter M5/M6 begins to switch. As this slews to either rail the transistors M2 or M3 are switched open, and then with a delay of about 10 ns the transistors M1 or M4, respectively, are switched closed. This latched feedback dumps enough charge on the comparator node to significantly speed the decision process, particularly at low current inputs.

Figure 4 Current Comparator Proposed in [12]. One disadvantage of this system is that the input node

slews from rail to rail and this can slow the operation of the comparator. However, this is still a significant speed improvement over a simple inverter comparator.

3. PROPOSED LOW-POWER HIGH SPEED CURRENT COMPARATOR ARCHITECTURE The proposed high speed, low DC offset, and low-

power consumption CMOS current comparator is shown in Figure 5. The current comparator consists of a current amplifier (M1-M4 and Rp), a Class B output stage (M7/M8), and three CMOS inverters (M5/M6, M9-M12). The proposed design is a modified version of the simple current comparator in [9], with an added current amplifier and an extra pair of inverters compared to the original design. The current amplifier enhances the

degrading response of the current comparator for small input currents. It consists of two current mirrors. To minimize power consumption, the widths of M1-M4 were kept to a minimum while the lengths were adjusted to achieve a desired current gain.

After matching the currents through M1 and M2, the currents were matched as well through M3 and M4. An additional Rp was added to minimize the DC current offset.

Dimensions of M1-M4 were chosen while taking into consideration the inverse relationship between the gain and the 3-dB frequency of the current amplifier. M5 and M6 are in the positive feedback loop and serve to invert the incoming signal. In order to reduce parasitic capacitances while allowing the inverter to draw more current for a faster charge, the lengths of M5 and M6 were minimized and their widths were adjusted. The dead-band region created by M7 and M8 is minimized by setting the lengths and widths of both transistors to a minimum. M9-M12 are a pair of CMOS inverters to output a rail-to-rail resulting signal with a negligible delay time.

Figure 5 New Current Comparator.

4. EXPERIMENTAL RESULTS

With HSPICE, the new current comparator was simulated using TSMC 0.35 μm CMOS technology parameters and with 1.8 V power supply. Figure 6 shows the input square-wave current which changes between -100 nA and 100 nA, as well as the transient waveform of output voltage of the proposed comparator and other three comparators discussed in the previous section. Incidentally, the rise and fall time delays of the new comparator are both 0.7 ns and the average power consumption is 130 μW.

Obviously, the solid line from the new circuit switches faster than the other cases. To our knowledge, the simulation results of the proposed current comparator are better than existing comparators to date. When the circuit in [9] was simulated, the delay was 1.7 ns for a 5 μA wave and 2.5 ns for a 1 μA wave. A major problem of this comparator is its response to low inputs. A large delay for small signals can jeopardize the performance of the current comparator. Instantaneous power of the proposed comparator is shown in Figure 7.

Figure 8 and Figure 9 compare the propagation delay

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No. 1, Decembe

different com2”, “3” and “4own in figureFigure 5, reefined as thee input signalations.

m Figure 8, ther than compaput current ann [11] for inp

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e delay time oarators in [9] nd is only sligput current lo

ation of the with others. Soparator is supeinput current. parator and oious section

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9

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that t has el in

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Since for mamparator at lowthe system, thprovement in wer dissipationtable and batte

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any applicatiow current lev

his circuit wilsystem level

n characteristiery supplied e

y Time Compari

nsumption Com

d comparators

the Proposed Co

ons, the respvel limits the l allow a signl designing. Aic of it is quitelectronics dev

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omparator.

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nificant speedAlso the lowte suitable forvices.

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f d d w r


10

Table 1 The Channel Length And Width Of Transistors Parameters Used For Current Comparators.

Träf [9] Lin [11] Banks [12] Our Approach Na

me W(μm)

L(μm)

Name

W(μm)

L(μm)

Name

W(μm)

L(μm)

Name

W(μm) L (μm)

M1 9 2 M1 0.5 0.35 M1 2 2 M1 0.36 0.48

M2 3 2 M2 1.2 0.35 M2 2 2 M2 0.36 2.04

M3 9 2 M3 0.5 0.6 M3 2 2 M3 0.36 0.24 M4 3 2 M4 0.5 0.7 M4 2 2 M4 0.36 0.6 M5 9 2 M5 2.5 1 M5 2 2 M5 1.08 0.24 M6 3 2 M6 0.6 1 M6 2 2 M6 0.36 0.24

M7 1.8 1 M7 2 2 M7 0.36 0.24

M8 0.8 0.35 M8 2 2 M8 0.36 0.24

M9 3.7 0.35 M9 1.08 0.24

M10 0.9 0.3

5 M10 0.36 0.24

M11 3.5 0.3

5 M11 1.08 0.24

Mn 1.9 0.35 M1

2 0.38 0.48

Mp 5 0.35

Table 2 Table Performance Comparisons Used for Current Comparators. Träf [9] Lin [11] Banks [12] Our Approach

Year 1992 2000 2008 2008 Power supply (V) 3 3 3 1.8 Technology (μm) 2 0.35 0.35 0.35

Minimum Input Current Amplitude (nA) 500 50 10 10

Propagation delay (nsec) 10 2.8 14 0.7 Power dissipation (μW)

390 (at 10 nA)

580 (at 100 nA)

300 (at 10 nA)

130 (at 10 nA)

REFERENCES

[1] D. J. Banks, P. Degenaar, and C. Toumazou, “A Colour and Intensity Contrast Segmentation Algorithm for Current Mode Pixel Distributed Edge Detection,” Eurosensors XIX, Barcelona, 2005.

[2] D. J. Banks, P. Degenaar, and C. Toumazou, “Distributed Current-Mode Image Processing Filters,” Electronics Letters, 41, pp. 1201–1202, 2005.

[3] H. Hassan, M. Anis, and M. Elmasry, “MOS Current Mode Circuits: Analysis, Design, and Variability,” IEEE Transactions on VLSI, vol. 13, no. 8, pp. 885-898, Aug. 2005.

[4] G. K. Balachandran and P. E. Allen, “Switched- Current Circuits in Digital CMOS Technology with

Low Charge-Injection Errors,” IEEE Journal of Solid-State Circuits, vol. 37, no. 10, pp. 1271-1281, Oct. 2002.

[5] V. Boonsobhak, A. Worapishet, and J. B. Hughes, “Reduced Kickback Regenerative Current Comparator for High-Speed Switched-Current Pipeline Analogue-To-Digital Converters”, Electronics Letters, vol. 39, no. 1, pp. 4-5, Jan. 2003.

[6] G. Palmisano and G. Palumbo, “Offset-Compensated Low Power Current Comparator,” Electronics Letters, vol. 30, no. 20, pp. 1637-1639, Sept. 1994.

[7] H. Lin, J. H. Huang, and S. C. Wong, “A simple high-speed low current comparator,” IEEE Trans. Circuit Syst., pp. 713-716, 2000.


11

[8] L. Ravezzi, D. Stoppaa, and G. F. Dalta Betta, “Simple High Speed CMOS Current Comparator,” Electronics Letters, vol. 33, no. 22, pp. 1829-1830, 1997.

[9] H. Träff, “Novel Approach to High Speed CMOS Current Comparators,” Electronics Letters, vol. 28, no. 3, pp. 310-312, 1992.

[10] A. T. K. TANG and C. TOUMAZOU, “High Performance CMOS Current Comparator,” Electronics Letters, 30, (l), pp. 5-6, 1994.

[11] H. Lin, J. H. Huang, and S. C. Wong, “A Simple High-Speed Low Current Comparator,” IEEE International Symposium on Circuits and Systems (ISCAS), Geneva, Switzerland, May 2000.

[12] D. Banks and C. Toumazou, “Low-Power High-Speed Current Comparator Design,” 31st Electronics Letters, vol. 44, no. 3, January 2008.


12

On the Search Window Updating for Occlusion Handling in Object Tracking Application

Mohammad Khansari, Hamid R. Rabiee, Mohammad H. Rohban, Mohammad Ghanbari

Abstract: Efficient search window updating mechanism has a great impact on the performance of object tracking applications. In this paper, adaptation and analysis of an inter-frame texture analysis for efficient occlusion han-dling is presented. The algorithm uses the temporal difference histogram of two successive frames to estimate the direction and speed of the object motion. This temporal texture analysis assists in tracking of the object under par-tial or short-term full occlusion. Experimental results show good performance in occlusion handling for object tracking compared to search window updating using the well known particle filters. Keywords: Interframe texture analysis, object tracking, occlusion handling, particle filters.

1. INTRODUCTION One of the challenging problems in the applied image

and 1 video processing is the tracking of objects. A typical object tracking application associates a model to the object of interest at a reference frame and then temporally tracks and updates this model in the successive frames of the video sequence.

Temporal object tracking applications aim at locating the target object in the successive frames based on the information about the object in the reference and the current frames. More specifically, it can be defined as the process of generating the trajectory of the object over time by locating it in successive frames of image sequence. One of the main difficulties in temporal tracking is finding the direction and displacement of the search window (SW). The SW defines the search area within the algorithm to look for the object at the current frame. The change of object location requires an efficient, smart and adaptive SW updating mechanism for at least three reasons:

• A proper SW location ensures that the object always lies within the search area and thus prevents losing the object.

• A location-adaptive fixed size SW reduces computational complexity by searching only the required area [1].

• If an object of interest is occluded by another object, intelligent positioning of the SW through the finding of the direction and the speed of the moving object may alleviate the occlusion [2, 3]. SW Updating based on the center of the bounding box

around the object at the current frame, will lead to Mohammad Khansari, Ph.D, Department of Information

Technology, Sharif University of Technology, International Cam-pus Kish Island, Iran. (e-mail: [email protected]).

H. R. Rabiee and M. Rohban are with the Digital Media Re-search Lab, Computer Eng. Department, Sharif University of Technology, Tehran, Iran.

M. Ghanbari is with the School of Computer Science and Elec-tronic Engineering, University of Essex, England.

propagation of any mismatch into the following frames to lose track, in particular for occluded objects [2].

Particle filtering techniques have recently proven to be powerful and reliable tools for tracking nonlinear/non-Gaussian systems [4, 5]. They allow fusion of different data to incorporate constrains and to account for different uncertainties. Furthermore, they are able to cope with missing data, e.g. lost pixels in a SW of an object tracking system.

Our approach to attain an efficient SW updating mechanism is to estimate the direction and the speed of motion of the moving object using inter-frame texture analysis technique to update the location of the SW [1, 2, 3, 6].

The main contribution of this paper is the adaptation of inter-frame texture analysis technique to cope with partial or long-term full occlusion of the object in tracking applications without assuming a model for the target of interest and providing the required analysis along with comparison against the well known particle filters.

We present the inter-frame texture analysis algorithm in section 2. Section 3 illustrates the experimental results and section 4 concludes the paper.

2. INTERFRAME TEXTURE ANALYSIS

Interframe texture analysis tries to estimate the SW

displacement by finding the direction and speed of the moving object inside the SW. To find the direction and the speed of the moving object, we define the temporal difference histogram of two successive frames. Coarse-ness and directionality of the frame difference of the two successive frames can be derived from the temporal dif-ference histogram. Finally, the direction and speed of the motion is estimated through the use of temporal differ-ence histogram, coarseness and directionality [1, 3]. 2.1. Temporal difference histogram

The temporal difference histogram of two successive frames is derived from the absolute difference of gray level values of the corresponding pixels at the two frames.


13

Consider the current search window ( ( , )tSA x y ) at

frame t

and a new search window ( 1( , )tSA x y+ )

determined by a displacement value of ( , )x yδ = Δ Δ to the current search window center in the next frame. It should be noted that the two search windows have the same size. We define absolute temporal difference

( ATDδ ) of the two windows as follows: 1( , ) | ( , ) ( , ) |t tATD x y SA x y SA x x y yδ += − + Δ + Δ (1)

Then, we calculate the histogram of the values of ATDδ . Note that the histogram has M bins, where M is the number of gray level values in each frame (256 for an 8 bit image, or pixels may be quantized into M levels).

Finally, the histogram values are normalized with respect to the number of pixels in the search window

( x yN N× ) to obtain the probability density of each gray

level value, ( )p iδ 0,.., 1i M= − . 2.2. Search window direction

Assume that the search window is a rectangular block. Consider eight different blocks at various directions with distance of iδ from the center of search window at the current frame (Figure 1).

Then, calculate the temporal difference

histogram, ipδ , for each block with respect to the

original block (search window). Now, we can easily compute the inverse difference moment, ,iIDM corresponding to each block using equation (2). The inverse difference moment, IDM , is the measure of homogeneity and is defined as:

1

20

( )1

M

i

p iIDM

iδ

−

=

=+∑ (2)

In a homogeneous image, there are very few dominant gray level transitions, hence i

pδ has a few entries of large magnitudes. Here IDM contains information on the distribution of the non-zero values of i

pδ and can be

used to identify the principle texture direction. If a texture is directional, that is coarser in one direction than the others, then the degree of the spread of the values in

ipδ should vary with the direction of iδ , assuming that

its magnitude is in the proper range. Thus, texture directionality can be analyzed by comparing spread measures of i

pδ for various directions of δ . To derive the motion direction from texture

direction, the direction that maximizes the IDM should be found.

{ }max max ,iIDM IDM= 1, 2,..,8i = (3)

The maximum value of IDM , maxIDM indicates that the frame difference is more homogenous in that direction than the others, implying that the corresponding blocks in the successive frames are more correlated. 2.3. Search window dispalcement

The quantitative measure for coarseness of texture is the temporal contrast which is defined as the moment of

inertia of pδ around the origin, and is given by:

12

0( )

M

iTCON i p iδ

−

=

= ∑ (4)

where M is the number of gray level values in each frame as stated in section 2.1.

The parameterTCON , gives a quantitative measure for the coarseness of the texture and its value depends on the amount of local variations that are present in the region of interest. The existence of high local variations in a frame implies an object activity in the frame and this frame is called active when compared to the frames with small variations. Since active frames of an image sequence exhibit a large amount of local variations, the temporal contrast derived from the frame difference signal is related to the picture activity. The parameter TCON is normalized to Local Contrast ( LCON ) in order to minimize the effect of size and texture of the search window ( SW ). The parameter LCON which defines the pixel variance within the search window is given by:

( ) 21 ,SW

LCON g x y gSW

⎡ ⎤= −⎣ ⎦∑ (5)

where ( ),g x y is the gray level value of the pixel

located at position ( ),x y and g is the average gray value of the pixels in the search window. Based on the temporal and local contrasts, a good estimation of the average motion speed, S within a block can be defined as:

TCONS kLCON

= (6)

Figure 1 Distance assignment in the differ-ent directions to find the maximum (Inverse Difference Moment)


14

where k is a constant with empirically selected values. The average motion speed, S, in equation (4), is not only independent of the size of the moving objects but also is invariant to the orientation of their texture. The value of S approaches to zero for stationary parts of the picture such as background, independent of its texture contents [1].

The displacement value of the search window for the next frame is given by

1 1 1j j jR S Disp− − −= −

1j j jDisp S R −⎢ ⎥= +⎣ ⎦ (7)

In some future frames the value of S might be less than 1, thus the displacement of the search window will be equal to zero. Parameter 1jR − denotes the displacement residue at the previous frame. Assuming low speed object movements, the parameter 1jR − , helps to sum up the values of displacements that are less than one pixel away, until they reach to at least one pixel displacement. 2.4. Occlusion handling analysis

Using Inter-frame texture analysis for search window updating mechanism keeps the object in search area during and after the occlusion. We analyze two different full occlusion cases; short-term and long-term occlusion scenarios.

• In case of short-term occlusion, motion directions of the object and the occluding object are different. Therefore, a suitable search window size is led to handle the occlusion as soon as the object partially re-appears.

• Long-term occlusion originates from the fact that the object of interest and the occluding object both moves at the same direction. Since the algorithm uses activity analysis to find the motion and direction of the search window and hence updates the search window location, it can predict the location of the object after occlusion. Therefore, our updating mechanism ensures that the object lies within the search window in case of occlusion and a robust target representation model allows for successful tracking, afterward.

3. Experimental results The inter-frame texture analysis algorithm was applied

on a sequence with a long term occlusion (data set S7, camera 1, from IEEE PETS2 2006 workshop). The SW size was set large enough, 212x177 (±78 pixels), to show how good the algorithm can locate the SW to handle the occlusion and encompass the target object of 56x21 pixels after the occlusion. Empirical parameters for the

2 Ninth IEEE International Workshop on Performance Evalua-tion of Tracking and Surveillance

algorithm were set to 4δ =

and k=16. All figures

(except for the reference frame) are zoomed in to show the bounding box and search window status better.

In the video sequence a full-occlusion starts at frame

645 and lasts for 200 frames. During this period there is no clear information available on the object location. However, our SW updating mechanism acts very well in this occlusion period and keeps the object within the search area during and after the occlusion (Figure 2(a, b, c, d)).

Long-duration occlusion normally occurs when the object of interest and the occluding object both moves at the same direction. Since the algorithm uses the activity analysis to find the motion and direction of the SW and updates the SW location, the SW follows the object even in the case of occlusion. Moreover, it avoids error propagation of false SW center prediction in the following frames in the case of incorrect object tracking of the current frame. Therefore, in contrast to the well-

a) Frame #506 b) Frame #646

c) Frame #830 d) Frame #855

500 550 600 650 700 750 800 8500

2

4

6

8

10

12

14

16

18

Frame Number

Dis

tanc

e (p

ixel

)

f)

Figure 2 (a-d) Search Window Updating in order to track a man with a long term occlusion (about 200 frames) using inter-frame texture analysis (f) Objective evaluation: distance between the center of tracked bounding box and the expected center for the inter-frame texture analysis algorithm.


15

known methods miss-tracking of occluded object is mostly prevented.

In short-duration occlusion, normally motion directions of the object and the occluding object are different. Despite the fact that SW moves in the opposite direction to the object, for a suitable SW size, the occlusion can be handled as soon as the object partially reappears.

Figure 2(f) shows the difference between the centre of gravity of the bounding box and the actual centre of the object in successive frames, as a measure of tracking fidelity (in pixels). There is an initial 18 pixel offset error which is mainly due to the large size of the bounding box. Note that, during the tracking the object has been tracked within the distance of the range of the initial offset. During the complete occlusion the distance measure is meaningless (we have deliberately set the distance to zero), but tracking is resumed with accuracy as good as the pre-occlusion period. The corresponding video clip of Figure 2 is available through the Internet3.

Particle filtering algorithm is also used to update SW, with 20 and 50 particles. State vector is 2 dimensional that is [Δx, Δy], where (Δx, Δy) is the change in upper left coordinates of SW. Dynamic process which used to update state vectors include adding a Gaussian noise with variance 6.0 to (Δx, Δy) each time.

Feature that was used during measurement process is a vector containing SW pixels grey values and SW edge map values. Measurement process is performed using weighted MSE of the previous and current window features as follows.

Probability{pt

i|features of previous frames} = exp{-|W×(Ft(pt

i)-At)|2/2σ2}

Where W is a weighting function which heavily emphasizes on the center of the window. pt

i is the ith particle at time t. pt-1

avg is the average of particles in time t-1 which were weighted according to their estimated probability. Ft(pt

i) is the feature corresponded to the window related to pt

i of the frame at time t. σ is chosen to be about 100. Weighting function is of Gaussian type with variance 20. At is a simple

3 http://ce.sharif.edu/~khansari/JASP/S7-T6-B.-WBMAdep3-sw78(212x177)-bs56x21-d4k16-00506-999.avi

appearance model formed by weighted averaging of chosen SW feature at time t-1 and At-1 :

At = aFt-1(pt-1avg) + (1-a)At-1

Where pt-1

avg is the mean of particles using the particle weights as the estimated probability. We have chosen a around 0.98 which means that appearance model changes very slowly. In order to find the new place of the SW in the current frame, average of {pt

1, pt2, …} = pt

avg is

calculated and used. However, particle filter completely miss-tracks the object of interest from frame number

a) Frame #506 b) Frame #646

Figure 3 Search window updating using particle filters without occlusion detection.

a) Frame #506 a) Frame #506

b) Frame #646 b) Frame #646

c) Frame #830 c) Frame #830

d) Frame #855 d) Frame #855

Figure 4 Search window updating using particle filters with occlusion detection (left: 20 particles, right: 50 particles).


16

646 and it will never place the SW in a position that contains the target of interest. The result for frame #646 is shown in Figure 3.

One solution to improve particle filter based search window displacement algorithm is to detect the occlusion and not to update particles weights during the occlusion period.

To detect occlusion we have adopted another appearance model Bt with the same definition as At but with another weight, say b. If MSE of features of SW corresponding to pt-1

avg and Bt is more than a threshold, an occlusion is reported. In the case of occlusion, particles weights will not be updated, until the end of occlusion. If the MSE falls below another threshold, the occlusion is considered to be ended. b is set around 0.94, which causes Bt to change faster than At. This adoption of b, makes the algorithm to detect the occlusion soon, before it can affect the main appearance model At. Results for 20 and 50 particles are shown in Figure 4. The figures have been zoomed in and cropped to show the search window better. As can be seen, occlusion detection and increasing the number of particles (right column) makes the results better; however it slows down the tracking process tremendously. Tuning of a, b, dynamic process variance, and σ is an important issue in the particle filter algorithm. The only way to set these parameters is by trial and error. They are highly depending on the video sequence nature and can not be set automatically. Incorrect choice of any parameter may cause the algorithm to misguide the search window and loss the object of interest. Therefore, regardless of quality of results of particle filter which follow the correct inter-frame texture analysis algorithm results in some case, the particle filter based search window updating algorithm is very sensitive to the parameters initialization and entails computational complexity which are far from practical applications requirements.

4. CONCLUSIONS A search window algorithm using inter-frame texture

analysis for search window updating in object tracking application has been presented. The algorithm finds the object motion and direction to displace the search window. The algorithm has been compared with the well known particle filtering algorithm for search window updating. The experimental results confirmed the efficiency of inter-frame texture analysis for displacement of search windows in tracking the object in case of occlusion. The algorithm outperforms particle filtering method for search window displacement and does not have the parameter sensitivity problem as well as the computational complexity of the particle filtering method.

REFERENCES [1] V. E. Seferidis and M. Ghanbari, “Adaptive Motion

Estimation Based on Texture Analysis”, IEEE Transactions on Communications, Vol. 42, No. 2-4, pp. 1277-1287, 1994.G.-D. Hong, “Linear controll-

able systems,” Nature, vol. 135, no. 5, pp. 18-27, July 1990.

[2] M. Khansari, H. R. Rabiee, M. Asadi, P. Khadem Hamedani, M. Ghanbari, “Adaptive Search Win-dow for Object Tracking in the Crowds using Un-decimated Wavelet Packet Features”, WAC, World Automation Congress, July 24-26, Budapest, Hun-gary, 2006.

[3] M. Khansari, H. R. Rabiee, M. Asadi, M. Ghanbari, “Occlusion handling for object tracking in crowded video scenes based on the Undecimated Wavelet features', ACS/IEEE International Conference on Computer Systems and Applications, AICCSA, May 13-16, Amman, Jordan, 2007.

[4] M.S. Arulampalam,. S. Maskell, N. Gordon, T. Clapp, “A tutorial on particle filters for online non-linear/non-Gaussian Bayesian tracking,” Signal Processing, IEEE Transactions on ,Volume 50, Is-sue 2, pp.174 - 188, Feb 2002.

[5] M. Isard, A. Blake, “CONDENSATION - condi-tional density propagation for visual tracking,” Int. J. Computer Vision, vol. 29, no. 1, pp. 5-28, 1998.

[6] M. Khansari, H. R. Rabiee, M. Asadi, M. Ghanbari, "Object Tracking in Crowded Video Scenes Based on the Undecimated Wavelet Features and Texture Analysis," EURASIP Journal on Advances in Sig-nal Processing, vol. 2008, Article ID 243534, 18 pages, 2008. doi:10.1155/2008/243534.


17

Composite Particleboard Panels Made from Stone powder-Rice straw Properties are Experimented as a New Construction Materials

Mohammad R. Shams , Hassan Pezeshki Modarres

Abstract: The objective of this research is to determine some of the properties of experimental multi layer particleboard panels made from powder stone and natural rice fiber, which are considered as invasive under-utilized species in Iran. Static bending, internal bond strength, the mixed concentration and particle size are studied. The samples are tested for their mechanical strength and physical stability properties according to the procedures defined by ASTM D-1037. The modulus of elasticity of the panels made from this method is 18 to 24.5% higher than the ones made from wood mixed particles upon particles size. Overall mechanical properties of the panels are not only more statistically advanced compare to pressed particle wood, also has much better results for the water soaking tests. This is demanding for environment of living on near water or environment of high humidity. This sandwich panel formed by two composite skins, one on each side of a core of insulation material has high shear resistance and light structures for the Earthquake zone, and is suitable for marine area with different climate and temperatures. Keywords: Particleboard, Stone Powder, Light Panels, Rice Straw, Fiber Surface Roughness, Shear strength

1. INTRODUCTION Natural fiber composites are alternatives for

replacement of the glass-fiber composite in many applications because of lower cost and lower density. Life biodegradability of components, lower pollutant emissions and enhanced energy recovery are some of the environmental advantages of the natural fibers. Natural fibers such as rice, cotton, bamboo, hay, jute and sisal are attracted for application in consumer goods and civil structures. These fibers have good thermal and acoustic insulating properties and have higher corrosion resistance to fracture [1, 2]. Relatively high strength and stiffness of the rice fiber is acceptable. The level of strength is not the same as glass fiber but most of the natural fibers such as rice have good ductility. The usage of rice fiber also has an economical advantage compare with glass or carbon fiber. The significant of this study is to explore the potential of abundant resources from the waste to use for fiber reinforced composite panels. Usage of the waste stone powder enhanced the strength, durability, workability and moisture uptake that can be suitable for marine area with different climate and temperatures.

Durability, desirable surface finishing and overlay is a premium product in cabinet and furniture manufacture as far as its quality is concerned. Usage of stone powder and overlaying with thin laminates of substrate composite panels such as rice fiber improve their appearance and properties resulting in value-added products. Rice particleboard panels provide an acceptable surface for the various applications, but well developed adhesive strength between overlay and substrate is required to have an ideal lamination process. Using powder stone can improve the adhesive strength and irregularities on the surface of substrate during the service life [3, 4].

Some of the major factors which play significant role for service life of the composite panels are particle size and geometry that play significant role to maintain the surface quality of overlaid products during its service life [5,6].

Other objectives of this research are to study the adhesive shear strength between stone powder and rice fiber substrate. The evaluation of the surface roughness of substrate panels and the strength of adhesive were investigated to improve the quality of laminating process.

2. MATERIALS AND METHODS

The raw material of the rice fiber, stone powder and

silicon solution matrix was selected as epoxy resin for the bonding. Rice straw and silicon resin was used to make the base substrate sheet panels and stone powder was applied as a single layer and double layers to study the composite panels mechanical properties. Preparation of rice fiber composite panels: The rice fiber and stone powders was collected from local resources (Isfahan, Iran).

__________

Manuscript has been presented at Fourth International Conference on Advances and Trends in Enginering Materials and their Applications (AES – ATEMA 2009 Hamburg).

Hassan Pezeshki Modarres, M.Sc student, Department of Chemical Engineering, Sharif University of Technology.

Mohammad Reza Shams, Department of Material Science, Sharif University of Technology, International Campus Kish Island, Iran. (Corresponding author to provide phone: +98-764-4422299 Ext: 334; fax: +98-764-4422828; e-mail: [email protected]).


18

Samples with a thickness of 10 mm were obtained from a designed mould. The samples were formed into 220 (L) x 110 (W) mm sheets for the experiments (Figure 1).

Five samples of A1, B1, C1, D1 and E1 groups were made in a long random fiber composite panel for the test. Five more panels were obtained from the rice fiber and silicon solution matrix in woven roving fiber reinforced composite panels (A2, B2, C2, D2, and E2 groups). The panels were applied with one side and two side stone powders (D and E group). The mould was made double-sided in a form of square shape. The long random fiber and woven roving fiber were obtained based on the mould size. The matrix was poured over the fiber, compressed and distribute evenly until it achieved the final thickness of 10 mm panel. For the single layer stone powder composite panels the same process is applied but 2.0 mm stone powder is distributed with the epoxy and is applied into the bottom of the mould first. Double-sided stone powder covering the rice panel substrate following the above process with additional 2.0 mm stone powder mixed with epoxy poured and distributed over the top of the samples. Unfinished composite plate then pressed and pushed down with the finger to avoid and eliminate the bubbles. The samples pressed with 0.5 Mpa pressure for 20 hours for the curing time at the room temperature condition of 25°C. All specimens of fiber composite panels are made this way with10 mm thickness. Test preparation: All specimens test were conditioned based on the standard procedures of ASTM D618-99 before mechanical tests. The test specimens were done in the room condition of 25oC in temperature and with relative humidity 40-45% in the standard laboratory atmosphere. A fine stylus profilometer, Bendix Model 400 was used to determine surface characteristics of the substrate for the adhesive shear strength of composite panels. Data Analysis: The densities of the specimens (Figure 2) were investigated. The types of mechanical test that were measured in this research are flexural tests. Each mechanical test was carried out based on natural fiber composite. Ten specimens were prepared for the

mechanical test. The results for each group of 1 and 2 samples are listed in Tables 1 and 2. Flexural test: Flexural test were performed on

Figure 1 Rice straw composite panels with and without powder stone

Table 1 The results of the densities for the long random (Group 1) and woven roving (Group 2) rice fiber specimens

Group 2 Density (g/cm3)

Group 1 Density (g/cm3)

Long random fiber(1) & Woven roving fiber (2) panels

0.72 0.76 10 wt % fiber (A1) & (A2)

0.69 0.73 15 wt % fiber (B1) & (B2)

0.58 0.69 20 wt % fiber (C1) & (C2)

0.82 0.89 Panel with one side powder (D1) & (D2)

0.91 0.97 Panel with two side Powder (E1) & (E2)

Table 2 The results of the Flexural strength of the long random (Group 1) and woven roving rice fiber specimens (Group 2)

2-Flexural strength (MPa)

1-Flexural strength (MPa)

Long random fiber (1) & Woven roving fiber (2) panels

105 79 10 wt % fiber (A1) & (A2)

114 87 15 wt % fiber (B1) & (B2)

107 82 20 wt % fiber (C1) & (C2)

117 97 Panel with one side powder (D1) & (D2)

102 85 Panel with two

side Powder (E1) & (E2)

Figure 2 Density (g/cm3) comparison of the Long random and woven roving rice straw panels

0

0.760.730.690.890.97

0

0.5

1

1.5

Long random and woven roving fiber densities

Group 2Density

Group 1Density


19

the same machine using the 3-point bending method according to ASTM D790-99 [7]. The specimens were tested at a crosshead speed of 1 mm/min.

3. RESULTS AND DISCUSSION

Density of the rice straw fiber composite: Density results indicate that long random rice panels (group 1) and woven roving panels (group 2) have close value of the density of a typical Oak wood (0.68 g/cm3). Adding powder stone to the substrate of rice straw panels ( samples D and E) indicate acceptable values of the densities of 0.82-0.91 g/cm3 which gives comprehensive information about overlaying characteristics of these composite panels (Table 1 and Figure 2) Long random rice straw fiber composite: With the increase of fiber loading percentages from 10% to 20wt. % long random rice fiber, the flexural strength value enhance regularly from 79 MPa and the density decrease from 0.76 g/cm3 to 0.69 g/cm3 as the weight percent of fiber increases (Figure 2 and Table 1). The condition above indicates that 10% and 15 wt. % long random fiber is stiff and strong, adding powder stone reveals that overall mechanical properties are enhanced but it has a limitation due to the size and thickness of rice straw panels and powder stone (Figures 3 and 4).

Woven roving rice straw fiber composite: The flexural properties of woven roving fiber reinforced composite indicates that 15 wt.% woven roving fiber reinforced epoxy composite has a maximum flexural strength of 105 MPa and Young’s modulus values of 4700 MPa. While 20 wt.% woven roving fiber has the values (Figures 3 and 4).

5. CONCLUSIONS

Density, mechanical properties such as flexural

strength, young's modulus was studied for the rice fiber reinforced composites panels.

Additional layers of powder stone was laminated on the substrate of rice straw panels. The properties are described on the basis of the experimental evidence, the conclusions are as follows:

The results of flexural strength test of the rice straw reinforced composites showed that 15 wt. % woven roving fiber has the highest value compared to other fiber content. Considering the density, the flexural strength test of the rice composites panels with 15 wt. % woven roving fiber and one side stone powder has the best results. Moreover, the particle size of the rice fiber, shear strength, adhesive bonding, water absorption and the influence of the different pressure level during the overlaying process are investigated and will be published for the next proceeding paper to have more comprehensive information about overlaying characteristics of these composite panels.

REFERENCES

[1] Oksman. K., M. Skrivars and J.F. Selin, 2003. Natu-ral fibers as reinforcement in polylactic acid (PLA) composites. J. Comp. Sci. Technol., 63:1317-1324.

[2] Joshi,S.V., L.T. Drzal and S.A. Mohanty, 2003. Are Natural fiber composites environmentally superior to glass fiber Reinforced composites: Applied Science and Manufacturing , 35: 371-376.

[3] Kolmann, F.F.P., E.W. Kuenzi and A.J. Stamm,

Figure 3 Flexural strength (MPa) comparison of the Long random and woven roving rice straw panels

105114107

117102

7987829785

0

50

100

150

Flexural stren

gth (M

Pa)

Long random and Woven …

Flexural strength (MPa)

Figure 4 Young's modulus (MPa) comparison of the long random and woven roving rice straw panels

470048504500

47004600

0100020003000400050006000

Youn

g's mod

ulus (M

Pa)

Long random and Woven roving composite panels

Young's modulus (MPa)

Table 3 The results of the Young's modules of the long random (Group 1) and woven roving rice fiber specimens (Group 2)

2-Young's modulus

(MPa)

1-Young's modulus

(MPa)

Long random fiber (1) & Woven roving fiber (2) panels

4700 4300 10 wt % fiber (A1) & (A2)

3850 3900 15 wt % fiber (B1) & (B2)

4500 4200 20 wt % fiber (C1) & (C2)

4700 4550 Panel with one side powder (D1) & (D2)

4600 4700 Panel with two side Powder (E1) & (E2)


20

1975. Principle of Wood Science and Technology. Wood Based Materials. Springer-Verlag, New York, Heilderberg and Berlin, vol: 2.

[4] Rabiej, R.J. and P.T. Brown, 1985. Factors affecting setting rate of polyvinyl acetate adhesives in wood joints. Proc. Conf.: Wood Adhesives in 1985: Status and Needs, Madison, WI.M. Young, The Technical Writer’s Handbook, Mill Valley, Seoul, 1989.

[5] Faust, T.D and J.T. Rice, 1986. Characterizing the Roughness of Southern Pine Veneer Surfaces. For-est Products J., 36: 45-48.

[6] Heebink, B.G., 1967. A procedure for quickly eva-luating dimensional stability of particleboard. Forest Products J., 17: 77-80.

[7] ASTM D790-99, 2000. Standard test method for flexural properties of un-reinforced and reinforced plastics and electrical insulating mate-rials.American Society for Testing Materials.


21

Modeling and Simulation of Graspers Force in Minimally Invasive Surgery

Ramin Hortamani, Abolghassem Zabihollah

Abstract: In Minimally Invasive Surgery (MIS) the operation is performed through introducing surgery instruments, graspers, and scissor into the body. In the present work, a novel smart grasper is presented in which the surgeon can virtually acquire a feeling of force/momentum experienced by the organ/tissue. The smart grasper uses piezoelectric sensors bonded at desired locations to detect the applied force/momentum by surgeon and to measure the transmitted response to the tissue/organ. Keywords: Minimally invasive surgery; grasper; force; piezoelectric;sensor.

1. INTRODUCTION Minimally invasive surgery (MIS) is a very new

approach in medical operations. It involves inserting special instruments into the body cavity through tiny incisions in order to perform surgical procedures (see Figure 1). Minimally invasive surgery (MIS) challenges the surgeon’s skills due to his separation from the operation area, which can only be reached with long instruments. Therefore, surgeon has not a sense of touch and sense of forces to recognize the material (tissue and organs) properties and thus apply proper force/momentum to avoid damage to the tissue/organ. To overcome these drawbacks, in the last few years many research works have been presented to provide the sense of touch in order to help the surgeon to recognize the material softness/hardness of the tissues.

Figure 1 Procedure of minimally invasive surgery.

Callaghan et al. [1] used direct measurement of

contact forces between a surgical instrument tips for

scissor blades and the tissue. Tavakoli et al. [2] designed a robotic master slave system for use in minimally invasive surgery. The system was capable of providing haptic feedback to the surgeon in all available degrees of freedom, thus, providing a sense of touch to the user. Sokhanvar et al. [3] proposed a sensor and modeled it for both analytically and numerically considerations. They examined a series of simulations performed in order to estimate the characteristics of the sensor in measuring the magnitude and position of a point load, distributed load, and the softness of the contact object. Shikida et al. [4] presented an active tactile sensor with ability to detect both contact force and hardness of an object simultaneously. Their system involved a diaphragm with a mesa (a flat-topped projection) at the center, a piezoelectric displacement sensor at the periphery, and a chamber for pneumatic actuation. Dargahi [5] proposed a prototype tactile sensing system with three sensing elements. The magnitude and position of the applied force were obtained by utilizing triangulation approach combined with membrane stress. Narayanan et al. [6] presented the design and fabrication of a micro machined piezoelectric endoscopic tactile sensor to determine the properties of tissues in minimally invasive surgery. Rosen et al. [7] developed a computerized force feedback endoscopic surgical grasper with computer control and a haptic user interface in order to regain the tactile and kinesthetic information that is lost. The system used standard unmodified grasper shafts and tips. The first steps in realizing soft tissue models through the development of an automated laparoscopic grasper and tissue cutting equipment to characterize grasping and cutting tasks in minimally invasive surgery were discussed by Tholey et al. [8]. Sjoerdsma et al. [9] measured the force transmission of laparoscopic grasping forces and bowel clamps and observed that the mechanical efficiency of the system is lower than 50%. Okamura et al. [10] developed an algorithm to simultaneously display translational and cutting forces for a realistic cutting simulation. They considered two cutting models: real tissue data, and analytical model.

Although, in the past decade many distinguished works have been presented in force/momentum sensing, however, still many problems remain unexplored which need to be thoroughly investigated before the idea of

__________ Manuscript has been presented at International Conference on

Bioinformatics and Biomedical Technology (ICBBT 2009) index by IEEE Computer Society.

Ramin Hortamani, M.Sc student, School of Science and Engineering, Sharif university of Technology, International Campus, Kish Island.

Abolghasem Zabihollah, Ph.D, Department of Mechatronics, Sharif University of Technology, International Campus Kish Island, Iran. (Corresponding author to provide phone: +98-764-4422299 Ext: 351; fax: +98-764-4422828; e-mail: [email protected]).


22

smart grasper can find its place in minimally invasive surgery. The works on giving sense of force to the surgeon are very scars and mostly limited for remote tele-operation and robotic surgery. According to the best knowledge of the authors, sensing force for common grasper has not been well studied. In the present work an in-depth comprehensive study has been performed in which surgeon can sense the amount of force applied to the tissue and organ.

2. GOVERNING EQUATION FOR A SMART GRASPER

Piezoelectricity is a coupling between a material’s

mechanical and electrical behaviors. When a piezoelectric material is squeezed, an electric charge collects on its surface (direct effect). Conversely, when a piezoelectric material is subjected to an electric field, it exhibits a mechanical deformation (converse effect. Applying an electric voltage to the electrodes of piezoelectric material will induce a mechanical deformation according to the magnitude and sign of applied voltage. These characteristics can potentially be utilized to develop and design smart graspers with capability to sense the force applied by the surgeon.

According to linear piezoelectric properties, the

governing equations are given by [11] as: For direct coupling:

D = [e]{ε}-[p]{E} (1) And for converse coupling,

σ = [C]{ε}-[e]{E} (2)

where {σ},{ε},{D} and {E} are the stress, strain , electric displacement and electric field vectors, and [C], [e]and [p] are the elasticity, piezoelectric and dielectric constant matrices, respectively. According to Equation (1), one can realize that mechanical strains produce electrical field which later can be used to provide as an indicator of sense of force.

3. FINITE ELEMENT ANALYSIS Real graspers integrated with sensors become very

complex instruments, thus, the electro-mechanical analysis of this relatively complex mechanisms using analytical approach is somehow infeasible. Here, a numerical approach based on finite element method is employed for this purpose.

The physical shape of a real grasper (Figure 2), can be simplified and approximated with a cantilever beam element.

According to the Kirchhoff’s hypothesis, the finite element model of the smart beam element can be given by [13]:

Figure 2 Model of a real grasper which use in MIS.

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−

−=

⎭⎬⎫

⎩⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡

}]{[)}({}]{[)}({

}{}{

][][][][

asas

asadd

sssssd

ssddd

KtQKtFd

KKKK

ψψ

ψ ψψ

ψ

ψψψ

ψ

(3)

where superscripts s and a stand for partitioned sub-

matrices in accordance with the sensory and actuator components, respectively. The left hand side includes the

unknown sensor voltage,{ }sψ , and the nodal displacements, { }d . The right hand side includes the applied mechanical load,{ ( )}dF t , applied voltage on the

actuator, { }aψ and the electric charge,{ ( )}sQ t . The present works deals with the sensing effect of piezoelectric elements, so, the actuator’s terms are removed from the above equation.

In this work the theoretical behavior of the system

under different loading conditions has been thoroughly examined. Modeling is performed based on a realistic grasper used for MIS. In order to model the loading effects, it is observed that when biological tissues are positioned between endoscopic grasper jaws, distributed loads sometimes mimic the actual grasping mechanism more closely [12]. However, in the case of a small contact area between the tissue and the surface of the grasper, the assumption of point or concentrated loads suffices in analyzing this phenomenon is adequate. In fact, the smaller the contact surface, the more realistic a concentrated load assumption is in the description of the grasping process [13]. In the next section numerical illustration are demostarted to show the concept of smart garsper and present the fontionality and performance of the system in sensing the applied force.

4. NUMERICAL ILLUSTRATIONS In this section, first the present finite element model

is validated using a simplified grasper modeled as a cantilever beam available in litrature, then, a more relastic grasper has been considered.

4.1Validating Example For the validation purpose, the simplified grasper

modeled as a cantilever beam described in Reference [13] is reexamined here. A cantilever beam integrated with a layer of piezoelectric sensor at the fixed point as

Research Bul

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25

REFERENCES [1] Dean J. Callaghan, and Mark M. McGrath,” A Force

Measurement Evaluation Tool for Telerobotic Cutting Applications: Development of an Effective Characterization Platform” International Journal of Mathematical, Physical and Engineering Sciences, Vol 1, no 3 (2007).

[2] M. Tavakoli, R.V. Patel and M. Moallem.” A Force Reflective Master-Slave System for Minimally Invasive Surgery”, Intl. Conference on Intelligent Robots and Systems, Las Vegas. Nevada, Oct 2003.

[3] S. Sokhanvar, M. Packirisamy and J. Dargahi, “A multifunctional PVDF-based tactile sensor for minimally invasive surgery, “Smart Mater. Struct. Vol.16 (2007) 989–998.

[4] Shikida M, Shimizu T, Sato K and Itoigawa K 2003 Active tactile sensor for detecting contact force and hardness of an object Sensors Actuators A, vol. 103 (2003) 213–8.

[5] J. Dargahi, “A piezoelectric tactile sensor with three sensing elements for robotic, endoscopic, and prosthetic applications”, Sensors and Actuators, vol. 80 (2000) pp 23–30.

[6] N. B. Narayanan, A. Bonakdar, J. Dargahi, M. Packirisamy and R. Bhat,” Design and analysis of a micromachined piezoelectric sensor for measuring the viscoelastic properties of tissues in minimally invasive surgery,” Smart Materials and Structures, vol. 15 (2006) pp.1684–1690.

[7] J. Rosen, B. Hannaford, M. P. MacFarlane, and M. N. Sinanan, “Force Controlled and Teleoperated Endoscopic Grasper for Minimally Invasive Surgery Experimental Performance Evaluation”, IEEE Transaction on Biomedical Enginering, vol. 46, no. 10, Oct (1999).

[8] G. Tholey, T. Chanthasopeephan, T. Hu, J. P. Desai, and A. Lau, “Measuring grasping and cutting forces for reality-based haptic modeling”, Pro.17th Int. Congress and Exhibition Computer Assisted Radiology and Surgery, London, U.K., vol. 1256, (2003) pp. 794-800.

[9] W.Sjoerdsma, JL.Herder, MJ.Horward, A.Jansen, J.Ban enberg,CA.Grimbergen, “Force transmission of laparoscopic grasping instruments”, Minimally Invasive Therapy & Allied Technologies vol. 6, no.4, (1997) pp 274-278.

[10] A. M. Okamura, R. J. Webster, J. T. Nolin, K. W. Johnson, and H. Jafry, “The haptic scissors: Cutting in virtual environments”, Proc. IEEE Int. Conf. Robot. Autom., Sep. (2003), pp. 828–833.

[11] Y.C. Fung, Biomechanics: Mechanical Properties of Living Tissues, 2nd ed., New York: Springer Verlag, (1993).

[12] J. Dargahi, S. Najarian, “An endoscopic force-position sensor grasper with minimum sensors”, Canadian Journal of Electrical and. Computer Engineering, vol. 28, no. 3/4, (2003) pp. 155-16.


26

Manipulability Analysis for Gimbal Driven Robotic Arms

Foad Mohammadi, Iman Hemmatian, Kambiz Ghaemi Osgouie

Abstract: Gimbal transmissions are non-linear direct transmissions and can be used in robotic arms replacing the traditional revolute joints. To investigate manipulability of robotic manipulators, the classical criterion of Manipulability Ellipsoid has been formulated. Thus by keeping a constant norm for robot joint torques vector, the effects of replacing some traditional revolute joints in robotic arms with Gimbal transmissions, have been analyzed. The results show that the magnitude of the maximum force applicable when employing Gimbal transmission can be considerably larger. Also, the joint angles in which this maximum occurs, can be adjusted, thanks to the behavior of Gimbal transmission. Two simple robotic arms – a 3R manipulator and Stanford Arm – are selected to investigate the effects of Gimbal transmissions. Keywords: Gimbal joint, Manipulability ellipsoid, Direct transmission, Maximum task-space force force

1. INTRODUCTION By the necessity of more accurate and efficient robotic systems in technology, the drive systems for robot manipulators became important. The problems associated with traditional transmission methods like gearboxes, such as friction, backlash and compliance, and the development of electric motors for robotic applications, led to a new design approach called direct-drive (DD) transmission [1]. In this method the shaft of the motor is directly coupled to the joint of the manipulator. The problem associated to this kind of transmission is that the weight of the motors attached to robot joints reduces the payload. So existence of an intermediate element that provides relocation becomes important, leading to a design, named direct transmission (DT) [2]. This transmission method has higher performance than DD, however, because dynamic complexities – like coupling and non-linearity – are directly reflected on the motor shaft, one cannot use common single-input single-output (SISO) control techniques to control the robot [3]. This problem can be resolved using more advanced control methods like model-based [4] or computed torque control [5]. Another way is to redesign the manipulator to reduce dynamic complexity [6]. Vines [2] proposed non-linear drive transmission which has the advantages of DT and reduction ratio properties of gearboxes. It has very little friction, backlash, and compliance compared to the traditional methods, while providing actuator relocation, directness of DD, and a varying reduction ratio. The Gimbal drive, shown in Figure 1, is a good example of non-linear direct transmission elements. It has all the advantages mentioned for DT mechanisms.

Gimbal mechanisms are also used in mechanical gyroscopes [12]. The Gimbal provides the gyroscope the freedom to rotate about its axis as the base of the

Manuscript has been presented at IEEE International Conference on Robotics and Biomimetics (ROBIO 2009).

Foad Mohammadi and Iman Hemmatian are with Sharif University of Technology – International Campus in Kish Island, Iran.

Kambiz Ghaemi Osgouie, Ph.D, Department of Mechatronics, Sharif University of Technology, International Campus Kish Island, Iran. (Corresponding author to provide phone: +98-764-4422299 Ext: 339; fax: +98-764-4422828; e-mail: [email protected]).

gyroscope is rotated. The Gimbal mechanisms are also implemented in control moment gyroscopes (CMG). CMGs have traditionally been used for attitude control of spacecraft. They are torque-generating mechanisms consisting of a rotating flywheel as well as a wheel-tilting actuator. These single- or double-Gimbaled actuators are typically used for agile, high-rate maneuvers. The use of this mechanism in Control Moment Gyroscopes (CMGs) is investigated in [8].

In this paper kinematics of a typical Gimbal transmission has been considered in the first section. The relations between input and output variables and adjustment parameters are investigated. In section III the concept of manipulability ellipsoid is formulated. It is employed to evaluate the manipulability of robotic arms. Manipulators with traditional revolute joints and those with Gimbal transmission joints are then compared in section IV. It is shown that employing Gimbal transmissions, increases the maximum applicable force at the desired work-point.

2. THE GIMBAL DRIVE In Figure 1 a typical, one degree-of-freedom, Gimbal

drive is shown. To describe its kinematics, the following matching function is considered [2]:

θ θ _ tan tan θ . cos θ (1)

Figure 1 The Gimbal drive


27

In which, is the input angle (angle of rotation of the vertical shaft), θ is the output angle (angle of rotation of the horizontal shaft/frame), is the gradient of the truncated cylinder, and _ is the offset angle at the output. In Figure 2 the input/output relationship for a Gimbal drive is shown, considering different truncation gradient angles. It can be deduced that the gradient of the truncated cylinder, , is a design parameter because it sets the range of output angle. As is increased, the range of the output angle, , increases. To get a desired range for the output angle, proper truncation gradient shall be taken. The derivative of the matching function (1), with respect to the input angle, , represents the reduction ratio ( ) of the Gimbal drive:

tan . sin1 tan . cos

(2)

In Figure 3a the variations of reduction ratio of the Gimbal transmission is shown versus the input angle for different values of . It can be seen that for positive values of the input angle, the reduction ratio becomes negative, thus changing the motion direction of the output angle. It is good to mention that for input angles equal to 90 and -90 degrees, reduction ratio has its minimum and maximum values, respectively. The sign of reduction ratio changes when the input shaft rotates 180 degrees. It is also shown that the maximum and minimum values of

reduction ratio increase as increases. The relationship between the reduction ratio and output

angle θ is shown in Figure 3b. It shows that for every output angle there are two reduction ratios associated with equal value and opposite sign, it is based on the fact that for every output angle there exist two input angle configurations (Figure 2). From eq. (1) and Figure 2 it can be deduced that for every output angle there are two possible input angles. However, Figure 3 a depicts that for every input angle, there exists just one reduction ratio. Thus for every output angle there should be two reduction ratios associated. This fact signifies the importance of as a design parameter. For greater values of , greater values of reduction ratios are achievable.

3. PROBLEM DECLARATION So far, the qualities of the single-input single-output

Gimbal transmission were discussed. Achieving adjustable transmission ratio, suggests using Gimbal transmissions in robotic arms to obtain a desired manipulability. That is to say, considering the workspace of a manipulator and the desired velocity/force values at certain work-points, one may employ Gimbal transmissions to the joints of the manipulator, to be able to adjust desired manipulability.

To investigate manipulability qualities, here the classical criterion of r ellipsoid is formulated for manipulators with traditional revolute joints. It is compared to the same quality of the same arm in which some joints are substituted with Gimbal transmissions.

In order to find the maximum achievable force at the manipulator’s tip point, the classical hypothesis is to assume that the Euclidian norm of the joint torques remains unity (this is to bound the joint torques) [9]. That is:

The relation of task-space force and the vector of joint torques is:

τ JT. F (4)In which J is the Jacobian of the whole manipulator.

By substituting (4) in (3), one obtains:

. . . 1 (5)

. 1 (3)

Figure 2 Transfer characteristics of Gimbal drive

Figure 3 Reduction ratio property of Gimbal drive: (a) reduction ratio vs. input angle, (b) reduction ratio vs. output angle


28

Relation (5) changes the hyper-sphere of the joint forces (3), into a hyper-ellipsoid that is called the resistivity ellipsoid. This method is largely employed to evaluate the manipulability qualities of robotic arms [10].

The aim here, is to achieve greater force in a specified direction using Gimbal mechanism. The task-space force vector is expressed as:

. . (6)

In which is the magnitude of and is a vector of unit length on the direction of .

Substituting (6) in (5) yields:

. . . . 1 (7)

Thus, one may determine the magnitude of the applicable force in a desired direction at a specific point of manipulator’s workspace. By comparing the magnitude of the applicable force, f , for different types of manipulators one is able to recognize the efficient robot design, and if using Gimbal transmissions instead of the traditional revolute joints improves robot’s manipulability.

4. CASE STUDIES Two cases are selected to investigate the effects of Gimbal transmissions in robotic manipulators. For each case a simple manipulator is considered and using eq. (7) its manipulability is investigated.

A. 3R Spatial Robot First a 3R spatial robot, shown in Figure 4, is

investigated to analyze the advantages of Gimbal drive in robot joint transmission.

Here, for convenience, … and … are used instead of and functions,

respectively. Assuming 0.5 , 1 , the Jacobian matrix of the 3R spatial robot, when all joints

are traditional revolute joints, is:

12

12

12

12

12

12

012

12

(8)

Now it is desired to use Gimbal transmissions at joints 2

and 3. To obtain new Jacobian matrix, one shall substitute from (1) for and of the 3R spatial robot. Assuming _ 0 , 45°, the Jacobian matrix for the manipulator with Gimbal transmissions at joints 2 and 3, becomes:

(9)

12 1

12 1 1

12 1

12 1

12 1 1

12 1

0

12 1 1 1

12 1

In which JG is the Jacobian of the manipulator when a Gimbal transmission is used at joints 2 and 3.

Figure 4 3R Spatial Manipulator [11]

Figure 5 Variations of the applicable force versus and , for the 3R robot with traditional revolute joints


29

Using Manipulability ellipsoid, we compare f for the simple and Gimbal equipped 3R spatial robots. Figure 5 shows variations of the applicable force, f , with respect to and , for the robot with traditional revolute joints when 0. The desired direction of the force vector is described as dx dy dz 1. Figure 6 shows variations of the applicable force, , versus joint variables θ and θ for the robot equipped with Gimbal transmission at joints 2 and 3.

By comparing Figure 5 and Figure 6, it can be deduced that the Gimbal equipped robot represents better behavior in terms of smoothness and higher achievable forces. Maximum force achievable for the simple 3R spatial robot is 1.9962 N and the corresponding value for the same robot with Gimbal transmissions at joints 2 and 3 is 2.4495 N. This means the same manipulator can exert greater forces when its simple revolute joints are substituted with Gimbal transmissions. Also it is considerable that for positions near 0 the magnitude of force is maximum when Gimbal transmissions are used and by getting far from this point it is decreased. As explained in section II, Gimbal transmissions are adjustable mechanisms. Thus the maximal point shown in Figure 6 can be set to occur in a desired work-point. This is one of the outstanding achievements of employing Gimbal transmissions.

B. Stanford Arm

Another case investigated here is the Stanford Arm shown in Figure 7.

The Jacobian matrix for Stanford Arm , assuming 1 , 0.5 is:

1212

0

(10)

Using Gimbal drive at joint 2 the new Jacobian matrix is

obtained by substituting from (1) for in (10). Assuming _ 0 , 45°, the Jacobian matrix is derived:

1

12 1 1 1

1

12 1 1 1

01

1

1

(11)

In which JG is the Jacobian of the manipulator when a Gimbal transmission is used at joint 2.

Again using Manipulability ellipsoid analysis, we compare the maximum applicable force, , for Stanford arm with traditional revolute joints and the arm equipped with Gimbal transmissions at revolute joint 2. To this end,

(the joint variable of the arm’s mere prismatic joint) is set to 1 . The direction of the desired force vector is assumed to be 1. Figure 8 shows variations of the applicable force, f , versus joint variables

and for Stanford Arm with traditional type joints. Figure 9 depicts variations of the applicable force, f , versus joint variables and for Stanford Arm equipped with Gimbal transmission at joint 2.

As it can be concluded from Figure 8 and Figure 9, the maximum applicable force exerted by the Gimbal equipped Stanford Arm is much greater than that without Gimbal transmission. The maximum value of f for the simple Stanford Arm is 1.7212 N while the corresponding value for the Stanford Arm with Gimbal at joint 2 is 5.3783 N.

Figure 6 Variations of the applicable force versus and , for the 3R robot with Gimbal transmissions at joints 2 and 3

Figure 7 Stanford Arm and corresponding joint frames and variables [12]


30

To have a better comparison between the two configurations, Figure 10 and Figure 11, show the variations of f versus when the joint variable of the first joint is kept 0 . As for the 3R robot discussed in previous part, it is considerable that when the joint is Gimbal equipped, maximum applicable force occurs at

0. The magnitude of the maximum force applicable when employing Gimbal transmission is considerably larger. Besides, one can set not only the joint angle in which this maximum occurs, but also the slope of the curve by which the applicable force decreases when is changed. These two settings are made by adjusting _ and respectively.

5. CONCLUSION The effects of using Gimbal mechanisms as non-linear

direct transmissions in robotic arms have been analyzed. The input/output behavior of the Gimbal drive and its reduction ratio were formulated. Depending on the application, different values of truncation gradient, , can be used to satisfy the range of output angle and reduction ratio needed. Another setting parameter is

_ by which one is able to adjust an offset for the Gimbal transmission. At the next step, the effects of Gimbal drive on the force exerted by robotic manipulators were investigated using Manipulability Ellipsoid. Two famous manipulators, 3R spatial robot and Stanford Arm were selected to investigate the effects of Gimbal transmissions. When some simple revolute joints were replaced with Gimbal transmissions, the maximum applicable force at the tip point of robot, were compared to the case with traditional revolute joints. Smoothness of the relation between input and output of the Gimbal drive shown represents the most desirable motion and force behavior for a manipulator. The results confirm that Gimbal drive is a novel design as a joint of a manipulator and can be optimized for improving the performance of robots based on their applications. The magnitude of the maximum force applicable when employing Gimbal transmission can be considerably larger. Besides, one can set not only the joint angles in which this maximum occurs, but also the slope of the curve by which the applicable force decreases when joint angles are changed.

Figure 8 Variations of the applicable force versus θ and θ , for Stanford Arm with traditional revolute joints

Figure 9 Variations of the applicable force versus and , for Stanford Arm with Gimbal transmission at joint 2

Figure 10 Variations of the applicable force versus θ ,

for Stanford Arm with traditional revolute joints

Figure 11 Variations of the applicable force versus θ , for Stanford Arm with Gimbal transmission at joint 2


31

These two settings are made by adjusting _ and respectively.

REFERENCES [1] H. Asada, K. Youcef-Toumi, Direct-drive robots:

theory and practice, Cambridge, MA: MIT Press, 1987.

[2] P.J. Turner, P. Nigrowsky, G. Vines, “A new approach for the design of robot joint transmission,” Mechatronics, vol. 11, 2001, pp. 1053-1062.

[3] H. Asada, K. Youcef-Toumi, “Analysis and design of a direct-drive arm with a five-bar-link parallel drive mechanism,” ASME Transactions: Journal of Dynamic Systems, Measurment, and Control, vol. 106, 1984, pp. 225–30.

[4] H. Berghuis, H. Nijmeijer, “A passivity approach to controller-observer design for robots”, IEEE Transactions on Robotics and Automation, vol. 9(6), 1993, pp. 740-754.

[5] CH An, CG Atkenson, JD Griffiths, JM Hollerbach, “Experimental evaluation of feed-forward and computed torque control,” IEEE Transactions on Robotics and Automation, vol. 5(3), 1989, pp. 368-373.

[6] H. Kazerooni, S. Kim, “On the design and construction of direct-drive robots,” ASME Transactions: Journal of Engineering for Industry, vol. 112, 1990, pp. 197–201.

[7] B. Siciliano, O. Khatib, Springer Handbook of Robotics, Springer-Verlag Berlin Heidelberg, 2008, pp. 478-485.

[8] Robert J. Bauer, “Kinematics and dynamics of a double-Gimbaled control moment gyroscope,” Mechanism and Machine Theory, vol. 37, 2002, pp. 1513-1529.

[9] J.P.Merlet, Parallel Robots, , Springer-Verlag Berlin Heidelberg, 2nd edition, , 2006, pp. 260-266.

[10] Kambiz Ghaemi Osgouie, Ali Meghdari, and Saeed Sohrabpour, Optimal configuration of dual-arm cam-lock robot based on task-space manipulability. In: Robatica 2008, Cambridge University Press.

[11] J. J. Craig, Introduction to Robotics: Mechanics and Control, Pearson Prentice Hall, 2005, pp. 90-100.

[12] K. S. Fu, R.C. Gonzalez, C.S.G. Lee, Robotics : Control, Sensing, Vision, And intelligence, MacGraw Hill, 1987, pp. 37-40.

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